7 papers
Platonic Transformers: A Solid Choice For Equivariance
Mohammad Mohaiminul Islam, Rishabh Anand, David R. Wessels +7
While widespread, Transformers lack inductive biases for geometric symmetries common in science and computer vision. Existing equivariant methods often sacrifice the efficiency and…
Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles
Siddharth Mehrotra, Jin Huang, Xuelong Fu +3
Background: Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT. However, current research often adopts techno-centric approaches, fo…
Longitudinal Flow Matching for Trajectory Modeling
Mohammad Mohaiminul Islam, Thijs P. Kuipers, Sharvaree Vadgama +4
Generative models for sequential data often struggle with sparsely sampled and high-dimensional trajectories, typically reducing the learning of dynamics to pairwise transitions. W…
SlicerNNInteractive: A 3D Slicer extension for nnInteractive
Coen de Vente, Kiran Vaidhya Venkadesh, Bram van Ginneken +1
SlicerNNInteractive integrates nnInteractive, a state-of-the-art promptable deep learning-based framework for 3D image segmentation, into the widely used 3D Slicer platform. Our ex…
Uncertainty-aware retinal layer segmentation in OCT through probabilistic signed distance functions
Mohammad Mohaiminul Islam, Coen de Vente, Bart Liefers +3
In this paper, we present a new approach for uncertainty-aware retinal layer segmentation in Optical Coherence Tomography (OCT) scans using probabilistic signed distance functions…
Zero-shot capability of SAM-family models for bone segmentation in CT scans
Caroline Magg, Hoel Kervadec, Clara I. Sánchez
The Segment Anything Model (SAM) and similar models build a family of promptable foundation models (FMs) for image and video segmentation. The object of interest is identified usin…